Martin Wynn, Janat Ilyas, Omer Faruk Isleyen, Henrik Brüntrup, Bilgin Metin
This paper examines the recent evolution of Enterprise Resource Planning (ERP) systems and explores the critical success factors (CSFs) for project implementation in the digital age. Adopting a qualitative inductive approach, the article first reports on CSFs evident in relevant literature drawn from the past two decades. In the second research phase, interview feedback from nine industry project managers is analysed to identify the CSFs now considered of particular relevance in the digital era. The article concludes that many of the established CSFs remain relevant, but recent research suggests the deployment of digital technologies and the availability of the cloud for ERP operation will mean that CSFs will be re-formulated in new technology and business environments. CSFs related to cloud-based vs on-premise software operation, system configuration and functionality trade-offs, and the integration of digital technologies into ERP products, are likely to emerge in the digital era. Future studies could profitably focus on these largely unresearched aspects of ERP projects, to which this article makes a small contribution that may provide a useful point of reference for subsequent studies.
{"title":"Reassessing Critical Success Factors for ERP Implementation in the Digital Era","authors":"Martin Wynn, Janat Ilyas, Omer Faruk Isleyen, Henrik Brüntrup, Bilgin Metin","doi":"10.54963/dtra.v3i2.297","DOIUrl":"https://doi.org/10.54963/dtra.v3i2.297","url":null,"abstract":"This paper examines the recent evolution of Enterprise Resource Planning (ERP) systems and explores the critical success factors (CSFs) for project implementation in the digital age. Adopting a qualitative inductive approach, the article first reports on CSFs evident in relevant literature drawn from the past two decades. In the second research phase, interview feedback from nine industry project managers is analysed to identify the CSFs now considered of particular relevance in the digital era. The article concludes that many of the established CSFs remain relevant, but recent research suggests the deployment of digital technologies and the availability of the cloud for ERP operation will mean that CSFs will be re-formulated in new technology and business environments. CSFs related to cloud-based vs on-premise software operation, system configuration and functionality trade-offs, and the integration of digital technologies into ERP products, are likely to emerge in the digital era. Future studies could profitably focus on these largely unresearched aspects of ERP projects, to which this article makes a small contribution that may provide a useful point of reference for subsequent studies.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dimitris Angelakis, E. Ventouras, Spyros Kostopoulos, Pantelis Asvestas
Brain-computer interfaces (BCIs) hold immense promise for human benefits, enabling communication between the brain and computer-controlled devices. Despite their potential, BCIs face significant cybersecurity risks, particularly from Bluetooth vulnerabilities. This study investigates Bluetooth vulnerabilities in BCIs, analysing potential risks and proposing mitigation measures. Various Bluetooth attacks such as Bluebugging, Bluejacking, Bluesnarfing, BlueBorne, Location Tracking, Man-in-the-Middle Attack, KNOB, BLESA and Reflection Attack are explored, along with their potential consequences on commercial BCI systems. Each attack is examined in terms of its modus operandi and effective mitigation strategies.
{"title":"Cybersecurity Issues in Brain-Computer Interfaces: Analysis of Existing Bluetooth Vulnerabilities","authors":"Dimitris Angelakis, E. Ventouras, Spyros Kostopoulos, Pantelis Asvestas","doi":"10.54963/dtra.v3i2.286","DOIUrl":"https://doi.org/10.54963/dtra.v3i2.286","url":null,"abstract":"Brain-computer interfaces (BCIs) hold immense promise for human benefits, enabling communication between the brain and computer-controlled devices. Despite their potential, BCIs face significant cybersecurity risks, particularly from Bluetooth vulnerabilities. This study investigates Bluetooth vulnerabilities in BCIs, analysing potential risks and proposing mitigation measures. Various Bluetooth attacks such as Bluebugging, Bluejacking, Bluesnarfing, BlueBorne, Location Tracking, Man-in-the-Middle Attack, KNOB, BLESA and Reflection Attack are explored, along with their potential consequences on commercial BCI systems. Each attack is examined in terms of its modus operandi and effective mitigation strategies.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ephraim O Nwoye, Abdulgafaar A Muslehat, C. Umeh, Samuel O Okodeh, Wai Lok Woo
According to WHO, about 1.86 million people in Nigeria and about 24 million people worldwide are living with schizophrenia, having symptoms varying from hallucination to delusion, and distorted speech and thinking. Schizophrenia is a life-long disorder with no cure and thus, patients need continuous management with medications and psychotherapy. However, due to various factors such as the cost of therapy, time consumption, lack of adequate health workers, the unwillingness of patients to engage, and the pandemic, there is a need for an effective alternate medium for providing cognitive behavioural therapy (CBT) to schizophrenia patients. This research aims to develop a chatbot, which is called SchizoBot, delivering CBT for augmented management of schizophrenia. CBT for schizophrenia details, along with FAQs of schizophrenia patients were collected and adopted into a conversational format for pre-processing and model development. The model was developed with artificial neural network (ANN) and trained with the dataset which was split into train-test data to optimize the performance of the model. The result of the ANN showed an accuracy score of 93.97% at 60:40 train-test data split with 200 epochs. This robust system which provides an optimized chatbot platform using ANN as the model classifier for CBT delivery is foreseen to be a windfall to clinicians and patients as an augmentative management tool for schizophrenia. This, therefore, is a relatively low-cost and easily accessible means to significantly improve the health of schizophrenia patients while assisting clinicians in therapy delivery and compensating for the lapses in the administration of CBT to schizophrenia patients.
{"title":"SchizoBot: Delivering Cognitive Behavioural Therapy for Augmented Management of Schizophrenia","authors":"Ephraim O Nwoye, Abdulgafaar A Muslehat, C. Umeh, Samuel O Okodeh, Wai Lok Woo","doi":"10.54963/dtra.v3i2.225","DOIUrl":"https://doi.org/10.54963/dtra.v3i2.225","url":null,"abstract":"According to WHO, about 1.86 million people in Nigeria and about 24 million people worldwide are living with schizophrenia, having symptoms varying from hallucination to delusion, and distorted speech and thinking. Schizophrenia is a life-long disorder with no cure and thus, patients need continuous management with medications and psychotherapy. However, due to various factors such as the cost of therapy, time consumption, lack of adequate health workers, the unwillingness of patients to engage, and the pandemic, there is a need for an effective alternate medium for providing cognitive behavioural therapy (CBT) to schizophrenia patients. This research aims to develop a chatbot, which is called SchizoBot, delivering CBT for augmented management of schizophrenia. CBT for schizophrenia details, along with FAQs of schizophrenia patients were collected and adopted into a conversational format for pre-processing and model development. The model was developed with artificial neural network (ANN) and trained with the dataset which was split into train-test data to optimize the performance of the model. The result of the ANN showed an accuracy score of 93.97% at 60:40 train-test data split with 200 epochs. This robust system which provides an optimized chatbot platform using ANN as the model classifier for CBT delivery is foreseen to be a windfall to clinicians and patients as an augmentative management tool for schizophrenia. This, therefore, is a relatively low-cost and easily accessible means to significantly improve the health of schizophrenia patients while assisting clinicians in therapy delivery and compensating for the lapses in the administration of CBT to schizophrenia patients.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siming Guo, Jun Zhang, Qi Liu, Hongyun Li, Rongjie Wang, Qianshan Fang, Haiyi Cai, Hao Zou, Jinmu Li
This research employs non-contact plantar 3D data scanning and gait analysis methodologies to establish a rehabilitation assistance system tailored for foot arch anomalies. The system utilizes a non-contact plantar 3D data model to mitigate dysfunctions within the plantar skeletal-muscular system. Its objectives include facilitating personalized remote diagnosis of foot arch anomalies, enabling patients to monitor their rehabilitation progress, and supporting at-home rehabilitation efforts. A dataset comprising 124 cases of physiological foot arch anomalies in adults aged 18 and above was collected and analyzed. The findings demonstrate the system’s flexibility, high spatial resolution, personalization, and innovation. Notably, the system achieves real-time measurement of positive pressure and shear force distribution at the plantar interface, facilitates the construction of accurate geometric models, and yields high-quality plantar three-dimensional coordinate data. This research contributes theoretical and technical underpinnings for the application of footwork anomaly diagnosis and correction.
{"title":"Study on the Effect of Three-dimensional Reconstruction Technique Based on Human Gait Plantar Transient Data on Rehabilitation of Patients with Abnormal Foot Arch","authors":"Siming Guo, Jun Zhang, Qi Liu, Hongyun Li, Rongjie Wang, Qianshan Fang, Haiyi Cai, Hao Zou, Jinmu Li","doi":"10.54963/dtra.v3i1.200","DOIUrl":"https://doi.org/10.54963/dtra.v3i1.200","url":null,"abstract":"This research employs non-contact plantar 3D data scanning and gait analysis methodologies to establish a rehabilitation assistance system tailored for foot arch anomalies. The system utilizes a non-contact plantar 3D data model to mitigate dysfunctions within the plantar skeletal-muscular system. Its objectives include facilitating personalized remote diagnosis of foot arch anomalies, enabling patients to monitor their rehabilitation progress, and supporting at-home rehabilitation efforts. A dataset comprising 124 cases of physiological foot arch anomalies in adults aged 18 and above was collected and analyzed. The findings demonstrate the system’s flexibility, high spatial resolution, personalization, and innovation. Notably, the system achieves real-time measurement of positive pressure and shear force distribution at the plantar interface, facilitates the construction of accurate geometric models, and yields high-quality plantar three-dimensional coordinate data. This research contributes theoretical and technical underpinnings for the application of footwork anomaly diagnosis and correction.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139600547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The variation of the contraction force associated with the phantom motion used for the actuation of a bionic upper-limb prosthesis represents a scenario encountered regularly by amputees, while prior research appears to not have been able to succinctly address this problem. In this study, an extended prosthesis control system is proposed which is able to recognise gesture intent motions alongside the prediction of an associated contraction force as part of an advanced pattern recognition system. As part of this research topic, this paper introduces the proposed control architecture and is based on the solving of the gesture recognition problem amidst varying contraction forces for a transradial amputee with a seemingly faint phantom motion.The work involves the application of a novel decomposition algorithm and the use of a set of computationally effective features, alongside the contrast of the recognition capabilities of the proposed approach using various classification models. The results show an enhanced recognition of gesture motion intent with the use of the decomposition method, despite the faint phantom motion signal from the amputee.
{"title":"On the Gesture Recognition of a Faint Phantom Motion for the Control of a Transradial Prosthesis amidst varying Contraction Forces","authors":"E. Nsugbe","doi":"10.54963/dtra.v2i1.93","DOIUrl":"https://doi.org/10.54963/dtra.v2i1.93","url":null,"abstract":"The variation of the contraction force associated with the phantom motion used for the actuation of a bionic upper-limb prosthesis represents a scenario encountered regularly by amputees, while prior research appears to not have been able to succinctly address this problem. In this study, an extended prosthesis control system is proposed which is able to recognise gesture intent motions alongside the prediction of an associated contraction force as part of an advanced pattern recognition system. As part of this research topic, this paper introduces the proposed control architecture and is based on the solving of the gesture recognition problem amidst varying contraction forces for a transradial amputee with a seemingly faint phantom motion.The work involves the application of a novel decomposition algorithm and the use of a set of computationally effective features, alongside the contrast of the recognition capabilities of the proposed approach using various classification models. The results show an enhanced recognition of gesture motion intent with the use of the decomposition method, despite the faint phantom motion signal from the amputee. ","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123695263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Endometriosis is a prevalent disease of the female endometrium which affects women of all ethnicities and has been seen to be most common in the 25–35 years age group. The disease does not have a definitive cure, hence care and management are the essential components towards dealing with the disease. At present, the predominant means towards the diagnosis of the presence of the disease involves different imaging modalities alongside laparoscopy, where the instrumentation is expensive to acquire and requires clinical expertise. Recently, work has been done by an author who leveraged Raman blood spectroscopy alongside machine learning towards an affordable high throughput means towards the prediction of endometriosis.This work utilises the Raman blood spectroscopy dataset alongside advanced signal processing, machine learning and clinical cybernetics, towards the design of a prediction machine which sits within a clinical framework to facilitate Human-Machine interaction for an enhanced care strategy for patients with endometriosis. The prediction machine is designed to initially predict whether a patient has the disease, and is then followed by the use of unsupervised learning to form an inference means towards predicting the extent of the disease. The results showed that a combination of the adopted methods could allow for a high prediction of the endometriosis disease. Subsequent work in this area would now include further optimisation of the prediction machine in order to potentially maximise the prediction accuracy.
{"title":"On the use of Raman Blood Spectroscopy and Prediction Machines for Enhanced Care of Endometriosis Patients","authors":"E. Nsugbe","doi":"10.54963/dtra.v2i1.94","DOIUrl":"https://doi.org/10.54963/dtra.v2i1.94","url":null,"abstract":"Endometriosis is a prevalent disease of the female endometrium which affects women of all ethnicities and has been seen to be most common in the 25–35 years age group. The disease does not have a definitive cure, hence care and management are the essential components towards dealing with the disease. At present, the predominant means towards the diagnosis of the presence of the disease involves different imaging modalities alongside laparoscopy, where the instrumentation is expensive to acquire and requires clinical expertise. Recently, work has been done by an author who leveraged Raman blood spectroscopy alongside machine learning towards an affordable high throughput means towards the prediction of endometriosis.This work utilises the Raman blood spectroscopy dataset alongside advanced signal processing, machine learning and clinical cybernetics, towards the design of a prediction machine which sits within a clinical framework to facilitate Human-Machine interaction for an enhanced care strategy for patients with endometriosis. The prediction machine is designed to initially predict whether a patient has the disease, and is then followed by the use of unsupervised learning to form an inference means towards predicting the extent of the disease. The results showed that a combination of the adopted methods could allow for a high prediction of the endometriosis disease. Subsequent work in this area would now include further optimisation of the prediction machine in order to potentially maximise the prediction accuracy.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116105388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jian Wang, Xinqi Shen, Mei Li, Quanbo Lu, Yixiao Yue
In view of the lack of unified data architecture and model of workshop transportation system components, this paper proposes the concept of the workshop transportation system functional unit based on asset administration shell (AAS). The designed workshop transportation system can solve the problem of unified modelling for different types of equipment, and realize the rapid construction and adjustment of the system. Meanwhile, the system has been applied in the specific workshop transportation system and enables well application results. In the functional unit of workshop transportation system, automatic markup language (AML) is applied. This paper constructs the AAS model based on the industrial 4.0 reference architecture model (RAMI4.0), and introduces the workshop transportation system AAS model and its realistic mapping as an example. The workshop transportation system functional unit AAS model based on the RAMI4.0 provides an effective solution for the standardization and integration of workshop transportation system components, which is no requirement to develop specific data conversion tools for this purpose. All transportation equipment with AAS can achieve information exchange and interoperability. It is conducive for the rapid implementation of workshop transportation system engineering and provides a guide for the establishment of intelligent workshop. Thereby, the proposed methodology demonstrates the flexibility and interoperability of AAS in smart manufacturing.
{"title":"Asset Administration Shell-Based Workshop Transportation System Design","authors":"Jian Wang, Xinqi Shen, Mei Li, Quanbo Lu, Yixiao Yue","doi":"10.54963/dtra.v2i1.73","DOIUrl":"https://doi.org/10.54963/dtra.v2i1.73","url":null,"abstract":"In view of the lack of unified data architecture and model of workshop transportation system components, this paper proposes the concept of the workshop transportation system functional unit based on asset administration shell (AAS). The designed workshop transportation system can solve the problem of unified modelling for different types of equipment, and realize the rapid construction and adjustment of the system. Meanwhile, the system has been applied in the specific workshop transportation system and enables well application results. In the functional unit of workshop transportation system, automatic markup language (AML) is applied. This paper constructs the AAS model based on the industrial 4.0 reference architecture model (RAMI4.0), and introduces the workshop transportation system AAS model and its realistic mapping as an example. The workshop transportation system functional unit AAS model based on the RAMI4.0 provides an effective solution for the standardization and integration of workshop transportation system components, which is no requirement to develop specific data conversion tools for this purpose. All transportation equipment with AAS can achieve information exchange and interoperability. It is conducive for the rapid implementation of workshop transportation system engineering and provides a guide for the establishment of intelligent workshop. Thereby, the proposed methodology demonstrates the flexibility and interoperability of AAS in smart manufacturing.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126866192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guangjie Wu, Chengyue Wang, Xiaojuan Huang, Quanbo Lu
Smart factories face the opportunities and challenges brought by globalization and new technologies, which requires flexible and practical approaches to lean manufacturing and optimization. The asset administration shell (AAS) is expected to help factories better cope with new challenges throughout the life cycle. This paper proposes a methodological framework for building AAS for the whole lifecycle of a smart factory. Based on the AAS methodological framework, the theory study of smart factories is developed.
{"title":"Design of Smart Factory Based on Asset Administration Shell","authors":"Guangjie Wu, Chengyue Wang, Xiaojuan Huang, Quanbo Lu","doi":"10.54963/dtra.v2i1.75","DOIUrl":"https://doi.org/10.54963/dtra.v2i1.75","url":null,"abstract":"Smart factories face the opportunities and challenges brought by globalization and new technologies, which requires flexible and practical approaches to lean manufacturing and optimization. The asset administration shell (AAS) is expected to help factories better cope with new challenges throughout the life cycle. This paper proposes a methodological framework for building AAS for the whole lifecycle of a smart factory. Based on the AAS methodological framework, the theory study of smart factories is developed.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125966169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The performance of computer programs (or the hardware that can be programmed such as IoTs, embedded computers, and PLCs) is generally based on the complexity of the particular program development technique. High complexity often uses more execution times and system resources. For this reason, the computer program is less computational complexity to be desired. The conditional statements tell computers what certain information is a major cause of computer program complexities, e.g., considering m desired conditions from n possible conditions. To achieve this aim in computer programs, the data combination is often utilized. However, it is high complexity. Moreover, they cannot give that one condition takes precedence over others. To rid these vulnerabilities of combined conditions, a simple programming technique for considering m desired conditions from n possible conditions is proposed in this work, which is based on the summation of the condition weights. It only has the complexity of search spaces and data constructions to be O(n) and each condition can be set to be different precedence from the others. Furthermore, the proposed technique is evaluated by extensive experiments. From the experimental results, they indicate that the proposed technique is more effective and efficient than the comparative technique.
{"title":"Programming Techniques for Considering m Desired Conditions from n Possible Conditions","authors":"Surapon Riyana, N. Homdoung, Kittikorn Sasujit","doi":"10.54963/dtra.v1i2.81","DOIUrl":"https://doi.org/10.54963/dtra.v1i2.81","url":null,"abstract":"The performance of computer programs (or the hardware that can be programmed such as IoTs, embedded computers, and PLCs) is generally based on the complexity of the particular program development technique. High complexity often uses more execution times and system resources. For this reason, the computer program is less computational complexity to be desired. The conditional statements tell computers what certain information is a major cause of computer program complexities, e.g., considering m desired conditions from n possible conditions. To achieve this aim in computer programs, the data combination is often utilized. However, it is high complexity. Moreover, they cannot give that one condition takes precedence over others. To rid these vulnerabilities of combined conditions, a simple programming technique for considering m desired conditions from n possible conditions is proposed in this work, which is based on the summation of the condition weights. It only has the complexity of search spaces and data constructions to be O(n) and each condition can be set to be different precedence from the others. Furthermore, the proposed technique is evaluated by extensive experiments. From the experimental results, they indicate that the proposed technique is more effective and efficient than the comparative technique.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126049342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seizures are a widespread condition affecting 50~65 million people in the world, and newborns are also susceptible to them. EEG is used to monitor the brain activity of newborns with suspected brain injuries, followed by a qualitative waveform interpretation by a group of clinical experts, where the means towards detection of seizures include a set of distinct characteristics in the waveform. This means of seizure detection has been critiqued, particularly due to subjectivity where, at times, waveform reviewing clinicians fail to reach a consensus on the presence of seizure activity in the brain of a newborn. As a means towards dealing with this problem, the author investigated the use of Artificial Intelligence-driven prediction machines capable of an automated diagnosis of seizure, based on a newborn’s EEG waveform. This approach used a reduced selection of EEG electrodes, the Linear Series Decomposition Learner (LSDL), an ensemble of a group of features, and performance comparison across multiple classification models. Secondary work was also carried out, which leveraged the patient information available alongside the EEG dataset. This involved the use of EEG towards predicting the level of asphyxia within the neonatal brain. The results from the seizure prediction exercise showed an increment in prediction performance of the seizures when preprocessed with the LSDL. The results spanned a range of figures (depending on the classification model), with the highest accuracy of 88.1%, while a probabilistic approach towards predicting the extent of seizures provided a maximum accuracy of 93.5%. The results from the secondary analysis showed a maximum accuracy for asphyxia prediction of 89.1%. The obtained results have helped to demonstrate that a reduced selection of electrode segments, alongside the selected algorithms, can serve towards the prediction of seizures for newborns within a neonatal intensive care unit.
{"title":"Artificial Intelligence-assisted Care for Human Newborns with Neurological Impairments","authors":"E. Nsugbe","doi":"10.54963/dtra.v1i2.67","DOIUrl":"https://doi.org/10.54963/dtra.v1i2.67","url":null,"abstract":"Seizures are a widespread condition affecting 50~65 million people in the world, and newborns are also susceptible to them. EEG is used to monitor the brain activity of newborns with suspected brain injuries, followed by a qualitative waveform interpretation by a group of clinical experts, where the means towards detection of seizures include a set of distinct characteristics in the waveform. This means of seizure detection has been critiqued, particularly due to subjectivity where, at times, waveform reviewing clinicians fail to reach a consensus on the presence of seizure activity in the brain of a newborn. As a means towards dealing with this problem, the author investigated the use of Artificial Intelligence-driven prediction machines capable of an automated diagnosis of seizure, based on a newborn’s EEG waveform. This approach used a reduced selection of EEG electrodes, the Linear Series Decomposition Learner (LSDL), an ensemble of a group of features, and performance comparison across multiple classification models. Secondary work was also carried out, which leveraged the patient information available alongside the EEG dataset. This involved the use of EEG towards predicting the level of asphyxia within the neonatal brain. The results from the seizure prediction exercise showed an increment in prediction performance of the seizures when preprocessed with the LSDL. The results spanned a range of figures (depending on the classification model), with the highest accuracy of 88.1%, while a probabilistic approach towards predicting the extent of seizures provided a maximum accuracy of 93.5%. The results from the secondary analysis showed a maximum accuracy for asphyxia prediction of 89.1%. The obtained results have helped to demonstrate that a reduced selection of electrode segments, alongside the selected algorithms, can serve towards the prediction of seizures for newborns within a neonatal intensive care unit.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115901596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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